Due to the significant influence of climate change and human activities on the water cycle, accurately estimating water availability has become imperative. This study introduces a time series model specifically crafted to decompose river flow time series, enabling estimation of trends, seasonality, and long memory components. Additionally, we incorporate this decomposition in quantile regression framework using a gamma function link. The estimation of this model is based Bayesian inference, exploring the computational efficiency and accuracy of Integrated Nested Laplace Approximations.
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Recacho et al. (2024) studied this question.
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